On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Computers and Operations Research - Special issue: implementing multiobjective optimization methods: behavioral and computational issues
Model-Based Decision Support Methodology with Environmental Applications
Model-Based Decision Support Methodology with Environmental Applications
Minimizing the sum of the k largest functions in linear time
Information Processing Letters
Modeling Decisions: Information Fusion and Aggregation Operators (Cognitive Technologies)
Modeling Decisions: Information Fusion and Aggregation Operators (Cognitive Technologies)
On Decision Support Under Risk by the WOWA Optimization
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
On optimization of the importance weighted OWA aggregation of multiple criteria
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Some properties of the weighted OWA operator
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computing rank dependent utility in graphical models for sequential decision problems
Artificial Intelligence
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The Reference Point Method (RPM) is an interactive technique formalizing the so-called quasi-satisficing approach to multiple criteria optimization. The DM's preferences are there specified in terms of reference (target) levels for several criteria. The reference levels are further used to build the scalarizing achievement function which generates an efficient solution when optimized. Typical RPM scalarizing functions are based on the augmented min-max aggregation where the worst individual achievement minimization process is additionally regularized with the average achievement. The regularization by the average achievement is easily implementable but it may disturb the basic min-max model. We show that the OWA regularization allows one to overcome this flaw since taking into account differences among all ordered achievement values. Further, allowing to define importance weights we introduce the WOWA enhanced RPM. Both the theoretical and implementation issues of the WOWA enhanced method are analyzed. Linear Programming implementation model is developed and proven.